ON-FIELD PERFORMANCE TEST AND CALIBRATION OTWO COMMERCIALLY AVAILABLE LOW-COST SENSODEVICES FOR CO<inf>2</inf> MONITORING

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Chojer, H (Author) Other; Branco, P (Author) FEUP; Martins, F (Author) Other; Sousa, SIV (Author) FEUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: International Journal of Environmental Impacts

Vol. 5

Pages: 15-22

ISSN: 23982640

Indexing

Scopus - 2 Citations

Crossref

ProQuest

DOAJ

Associated Projects

LEPABE - Financiamento Plurianual 2020-2023 da Unidade de I&D LEPABE - Laboratório de Engenharia de Processos, Ambiente, Biotecnologia e Energia

SENSINAIR - Sensores de baixo custo como ferramenta para redução da poluição do ar em infantários e escolas primárias – impacto na asma infantil

2SMART - 2SMART - engineered Smart materials for Smart citizens

Other information

Authenticus ID: P-00Z-901

DOI: 10.2495/ei-v5-n1-15-22

Abstract (EN): The use of low-cost devices for air quality monitoring is rapidly growing, and the reason behind the growth might (at least partially) be the real-time monitoring at a lower fixed and operating cost, ease of use and portability. Nevertheless, the poor data reliability of low-cost sensors (LCS) remains a consid-erable challenge, especially when deployed in real-world conditions. This study aimed to evaluate and improve the performance of two commercially available indoor air quality monitoring LCS devices: AirVisual Pro and uRAD Monitor A3 (uRAD), which were used to monitor CO2 via non-dispersive infrared technology. The analysis took place from June to July 2019 in several classrooms of an urban school in Porto city. Machine learning techniques such as multivariate linear, support vector, gradient boosting and XGBoost regression models were used to perform an on-field calibration for improving the data accuracy of the devices. The results showed that although both the devices showed a strong linear correlation (r > 0.9) with the reference device, they might indicate deviated CO2 concentrations if used in their advertised plug and play format. Specifically, uRAD showed a steady offset compared to the reference values, while AirVisual Pro showed lower deviations than uRAD. The on-field calibration models improved the reliability and showed low root mean square error values (around 30 mg/m3) and a high coefficient of determination (0.99).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7